Not Lost In Translation—Spatiotemporally-Aligned Human-Machine Interfaces Through IoT-Integrated Augmented Reality

A human-machine interface (HMI) is crucial for industrial workers to set up, monitor, control, and troubleshoot industrial machines. However, many modern HMIs are still difficult to use and learn because they can be cluttered, have inconsistent designs, or are difficult to map to the pertinent physical equipment. This paper posits that these issues arise due to the lack of alignment between data, controls, and machine functions, causing information overload and confusion as workers. This paper proposes an alternative AR interface integrated with Internet of Things (IoT) for efficient and intuitive machine status monitoring and control. The IoT-integrated AR interface is empirically validated through a laboratory experiment in a cyber-physical manufacturing facility, involving twenty-one participants. The study measured the relative effectiveness of the AR interface to the built-in HMI in terms of task completion times, error rates, help requests, and perceived workload using the NASA-TLX questionnaire. Correlations between the different performance metrics were analyzed to understand the relationships between task efficiency, accuracy, and user experience in industrial settings. The results demonstrated that the IoT-integrated AR interface significantly outperformed the traditional 2D HMIs across all measured metrics. Furthermore, the findings underscore the potential of AR to enhance operational efficiency and user satisfaction, particularly for untrained or novice users. The insights gained from this study can guide the design and implementation of future AR systems, ensuring they are both effective and user-friendly.